1. Field of the Invention
The present invention relates in general to object detection and tracking, and in particular to a system and method for statistically comparing and matching plural sets of digital data.
2. Related Art
Applications for automatic digital object detection and tracking, image registration, pattern recognition and computer vision analysis are becoming increasingly important for providing new classes of services to users based on assessments of the object's presence, position, trajectory, etc. These assessments allow advanced and accurate digital analysis (such as pattern recognition, motion analysis, etc.) of the objects in a scene, for example, objects in a sequence of images of a video scene. Plural objects define each image and are typically nebulous collections of pixels, which satisfy some property. These pixels could be the result of some pre-processing operation such as filtering, equalization, edge or feature detection, applied to raw input images. Each object can occupy a region or regions within each image and can change their relative locations throughout subsequent images of the video scene. These objects are considered moving objects, which form motion within a video scene and can be automatically detected and tracked with various techniques, one being template matching.
Template matching is a class of computer algorithms that is used in many digital computer applications, such as image registration, pattern recognition and computer vision applications. A template matching algorithm defines a function (for example, a metric) that estimates the similarity between sets of digital data. In this case, one set of digital data is commonly referred to as a template and another set of digital data is referred to as an image, wherein the template is typically smaller than the image (for instance, the template can be a small portion of the image). In computer vision applications, the template usually represents an object of the image that is being tracked and detected (located) within the image. The object can be located by computing the metric at various locations (u, v) in the image and determining where the metric is maximized.
However, many systems that use template matching are not robust or flexible enough for advanced image registration, pattern recognition and computer vision applications due to unfavorable tradeoffs of functionality for performance (for example, restricting themselves to translations of the template). Therefore, what is needed is a system and method for comparing and matching multiple sets of data by transforming one set of data and performing statistical analyses on the multiples sets of data. Whatever the merits of the above mentioned systems and methods, they do not achieve the benefits of the present invention.